wehub-resource-sync
780 lines
24 KiB
Python
780 lines
24 KiB
Python
from __future__ import absolute_import
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import os, sys
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import pickle as pkl
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import networkx as nx
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import numpy as np
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import scipy.sparse as sp
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from .. import backend as F
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from ..convert import graph as dgl_graph
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from ..utils import retry_method_with_fix
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from .dgl_dataset import DGLBuiltinDataset
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from .utils import (
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_get_dgl_url,
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deprecate_function,
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deprecate_property,
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download,
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extract_archive,
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generate_mask_tensor,
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get_download_dir,
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load_graphs,
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load_info,
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makedirs,
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save_graphs,
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save_info,
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)
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class KnowledgeGraphDataset(DGLBuiltinDataset):
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"""KnowledgeGraph link prediction dataset
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The dataset contains a graph depicting the connectivity of a knowledge
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base. Currently, the knowledge bases from the
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`RGCN paper <https://arxiv.org/pdf/1703.06103.pdf>`_ supported are
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FB15k-237, FB15k, wn18
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Parameters
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-----------
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name : str
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Name can be 'FB15k-237', 'FB15k' or 'wn18'.
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reverse : bool
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Whether add reverse edges. Default: True.
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raw_dir : str
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Raw file directory to download/contains the input data directory.
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Default: ~/.dgl/
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force_reload : bool
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Whether to reload the dataset. Default: False
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verbose : bool
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Whether to print out progress information. Default: True.
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access.
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"""
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def __init__(
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self,
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name,
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reverse=True,
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raw_dir=None,
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force_reload=False,
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verbose=True,
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transform=None,
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):
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self._name = name
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self.reverse = reverse
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url = _get_dgl_url("dataset/") + "{}.tgz".format(name)
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super(KnowledgeGraphDataset, self).__init__(
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name,
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url=url,
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raw_dir=raw_dir,
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force_reload=force_reload,
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verbose=verbose,
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transform=transform,
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)
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def download(self):
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r"""Automatically download data and extract it."""
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tgz_path = os.path.join(self.raw_dir, self.name + ".tgz")
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download(self.url, path=tgz_path)
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extract_archive(tgz_path, self.raw_path)
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def process(self):
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"""
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The original knowledge base is stored in triplets.
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This function will parse these triplets and build the DGLGraph.
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"""
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root_path = self.raw_path
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entity_path = os.path.join(root_path, "entities.dict")
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relation_path = os.path.join(root_path, "relations.dict")
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train_path = os.path.join(root_path, "train.txt")
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valid_path = os.path.join(root_path, "valid.txt")
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test_path = os.path.join(root_path, "test.txt")
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entity_dict = _read_dictionary(entity_path)
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relation_dict = _read_dictionary(relation_path)
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train = np.asarray(
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_read_triplets_as_list(train_path, entity_dict, relation_dict)
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)
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valid = np.asarray(
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_read_triplets_as_list(valid_path, entity_dict, relation_dict)
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)
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test = np.asarray(
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_read_triplets_as_list(test_path, entity_dict, relation_dict)
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)
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num_nodes = len(entity_dict)
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num_rels = len(relation_dict)
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if self.verbose:
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print("# entities: {}".format(num_nodes))
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print("# relations: {}".format(num_rels))
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print("# training edges: {}".format(train.shape[0]))
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print("# validation edges: {}".format(valid.shape[0]))
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print("# testing edges: {}".format(test.shape[0]))
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# for compatability
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self._train = train
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self._valid = valid
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self._test = test
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self._num_nodes = num_nodes
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self._num_rels = num_rels
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# build graph
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g, data = build_knowledge_graph(
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num_nodes, num_rels, train, valid, test, reverse=self.reverse
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)
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(
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etype,
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ntype,
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train_edge_mask,
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valid_edge_mask,
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test_edge_mask,
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train_mask,
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val_mask,
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test_mask,
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) = data
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g.edata["train_edge_mask"] = train_edge_mask
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g.edata["valid_edge_mask"] = valid_edge_mask
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g.edata["test_edge_mask"] = test_edge_mask
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g.edata["train_mask"] = train_mask
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g.edata["val_mask"] = val_mask
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g.edata["test_mask"] = test_mask
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g.edata["etype"] = etype
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g.ndata["ntype"] = ntype
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self._g = g
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@property
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def graph_path(self):
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return os.path.join(self.save_path, self.save_name + ".bin")
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@property
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def info_path(self):
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return os.path.join(self.save_path, self.save_name + ".pkl")
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def has_cache(self):
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if os.path.exists(self.graph_path) and os.path.exists(self.info_path):
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return True
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return False
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def __getitem__(self, idx):
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assert idx == 0, "This dataset has only one graph"
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if self._transform is None:
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return self._g
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else:
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return self._transform(self._g)
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def __len__(self):
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return 1
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def save(self):
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"""save the graph list and the labels"""
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save_graphs(str(self.graph_path), self._g)
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save_info(
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str(self.info_path),
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{"num_nodes": self.num_nodes, "num_rels": self.num_rels},
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)
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def load(self):
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graphs, _ = load_graphs(str(self.graph_path))
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info = load_info(str(self.info_path))
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self._num_nodes = info["num_nodes"]
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self._num_rels = info["num_rels"]
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self._g = graphs[0]
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train_mask = self._g.edata["train_edge_mask"].numpy()
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val_mask = self._g.edata["valid_edge_mask"].numpy()
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test_mask = self._g.edata["test_edge_mask"].numpy()
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# convert mask tensor into bool tensor if possible
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self._g.edata["train_edge_mask"] = generate_mask_tensor(
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self._g.edata["train_edge_mask"].numpy()
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)
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self._g.edata["valid_edge_mask"] = generate_mask_tensor(
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self._g.edata["valid_edge_mask"].numpy()
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)
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self._g.edata["test_edge_mask"] = generate_mask_tensor(
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self._g.edata["test_edge_mask"].numpy()
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)
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self._g.edata["train_mask"] = generate_mask_tensor(
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self._g.edata["train_mask"].numpy()
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)
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self._g.edata["val_mask"] = generate_mask_tensor(
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self._g.edata["val_mask"].numpy()
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)
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self._g.edata["test_mask"] = generate_mask_tensor(
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self._g.edata["test_mask"].numpy()
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)
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# for compatability (with 0.4.x) generate train_idx, valid_idx and test_idx
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etype = self._g.edata["etype"].numpy()
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self._etype = etype
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u, v = self._g.all_edges(form="uv")
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u = u.numpy()
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v = v.numpy()
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train_idx = np.nonzero(train_mask == 1)
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self._train = np.column_stack(
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(u[train_idx], etype[train_idx], v[train_idx])
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)
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valid_idx = np.nonzero(val_mask == 1)
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self._valid = np.column_stack(
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(u[valid_idx], etype[valid_idx], v[valid_idx])
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)
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test_idx = np.nonzero(test_mask == 1)
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self._test = np.column_stack(
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(u[test_idx], etype[test_idx], v[test_idx])
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)
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if self.verbose:
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print("# entities: {}".format(self.num_nodes))
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print("# relations: {}".format(self.num_rels))
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print("# training edges: {}".format(self._train.shape[0]))
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print("# validation edges: {}".format(self._valid.shape[0]))
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print("# testing edges: {}".format(self._test.shape[0]))
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@property
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def num_nodes(self):
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return self._num_nodes
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@property
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def num_rels(self):
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return self._num_rels
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@property
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def save_name(self):
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return self.name + "_dgl_graph"
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def _read_dictionary(filename):
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d = {}
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with open(filename, "r+") as f:
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for line in f:
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line = line.strip().split("\t")
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d[line[1]] = int(line[0])
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return d
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def _read_triplets(filename):
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with open(filename, "r+") as f:
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for line in f:
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processed_line = line.strip().split("\t")
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yield processed_line
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def _read_triplets_as_list(filename, entity_dict, relation_dict):
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l = []
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for triplet in _read_triplets(filename):
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s = entity_dict[triplet[0]]
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r = relation_dict[triplet[1]]
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o = entity_dict[triplet[2]]
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l.append([s, r, o])
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return l
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def build_knowledge_graph(
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num_nodes, num_rels, train, valid, test, reverse=True
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):
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"""Create a DGL Homogeneous graph with heterograph info stored as node or edge features."""
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src = []
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rel = []
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dst = []
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raw_subg = {}
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raw_subg_eset = {}
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raw_subg_etype = {}
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raw_reverse_sugb = {}
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raw_reverse_subg_eset = {}
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raw_reverse_subg_etype = {}
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# here there is noly one node type
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s_type = "node"
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d_type = "node"
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def add_edge(s, r, d, reverse, edge_set):
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r_type = str(r)
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e_type = (s_type, r_type, d_type)
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if raw_subg.get(e_type, None) is None:
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raw_subg[e_type] = ([], [])
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raw_subg_eset[e_type] = []
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raw_subg_etype[e_type] = []
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raw_subg[e_type][0].append(s)
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raw_subg[e_type][1].append(d)
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raw_subg_eset[e_type].append(edge_set)
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raw_subg_etype[e_type].append(r)
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if reverse is True:
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r_type = str(r + num_rels)
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re_type = (d_type, r_type, s_type)
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if raw_reverse_sugb.get(re_type, None) is None:
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raw_reverse_sugb[re_type] = ([], [])
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raw_reverse_subg_etype[re_type] = []
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raw_reverse_subg_eset[re_type] = []
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raw_reverse_sugb[re_type][0].append(d)
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raw_reverse_sugb[re_type][1].append(s)
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raw_reverse_subg_eset[re_type].append(edge_set)
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raw_reverse_subg_etype[re_type].append(r + num_rels)
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for edge in train:
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s, r, d = edge
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assert r < num_rels
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add_edge(s, r, d, reverse, 1) # train set
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for edge in valid:
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s, r, d = edge
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assert r < num_rels
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add_edge(s, r, d, reverse, 2) # valid set
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for edge in test:
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s, r, d = edge
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assert r < num_rels
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add_edge(s, r, d, reverse, 3) # test set
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subg = []
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fg_s = []
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fg_d = []
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fg_etype = []
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fg_settype = []
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for e_type, val in raw_subg.items():
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s, d = val
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s = np.asarray(s)
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d = np.asarray(d)
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etype = raw_subg_etype[e_type]
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etype = np.asarray(etype)
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settype = raw_subg_eset[e_type]
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settype = np.asarray(settype)
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fg_s.append(s)
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fg_d.append(d)
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fg_etype.append(etype)
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fg_settype.append(settype)
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settype = np.concatenate(fg_settype)
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if reverse is True:
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settype = np.concatenate([settype, np.full((settype.shape[0]), 0)])
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train_edge_mask = generate_mask_tensor(settype == 1)
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valid_edge_mask = generate_mask_tensor(settype == 2)
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test_edge_mask = generate_mask_tensor(settype == 3)
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for e_type, val in raw_reverse_sugb.items():
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s, d = val
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s = np.asarray(s)
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d = np.asarray(d)
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etype = raw_reverse_subg_etype[e_type]
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etype = np.asarray(etype)
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settype = raw_reverse_subg_eset[e_type]
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settype = np.asarray(settype)
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fg_s.append(s)
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fg_d.append(d)
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fg_etype.append(etype)
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fg_settype.append(settype)
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s = np.concatenate(fg_s)
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d = np.concatenate(fg_d)
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g = dgl_graph((s, d), num_nodes=num_nodes)
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etype = np.concatenate(fg_etype)
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settype = np.concatenate(fg_settype)
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etype = F.tensor(etype, dtype=F.data_type_dict["int64"])
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train_edge_mask = train_edge_mask
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valid_edge_mask = valid_edge_mask
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test_edge_mask = test_edge_mask
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train_mask = (
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generate_mask_tensor(settype == 1)
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if reverse is True
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else train_edge_mask
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)
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valid_mask = (
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generate_mask_tensor(settype == 2)
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if reverse is True
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else valid_edge_mask
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)
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test_mask = (
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generate_mask_tensor(settype == 3)
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if reverse is True
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else test_edge_mask
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)
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ntype = F.full_1d(
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num_nodes, 0, dtype=F.data_type_dict["int64"], ctx=F.cpu()
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)
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return g, (
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etype,
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ntype,
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train_edge_mask,
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valid_edge_mask,
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test_edge_mask,
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train_mask,
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valid_mask,
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test_mask,
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)
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class FB15k237Dataset(KnowledgeGraphDataset):
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r"""FB15k237 link prediction dataset.
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FB15k-237 is a subset of FB15k where inverse
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relations are removed. When creating the dataset,
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a reverse edge with reversed relation types are
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created for each edge by default.
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FB15k237 dataset statistics:
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- Nodes: 14541
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- Number of relation types: 237
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- Number of reversed relation types: 237
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- Label Split:
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- Train: 272115
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- Valid: 17535
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- Test: 20466
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Parameters
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----------
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reverse : bool
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Whether to add reverse edge. Default True.
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raw_dir : str
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Raw file directory to download/contains the input data directory.
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Default: ~/.dgl/
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force_reload : bool
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Whether to reload the dataset. Default: False
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verbose : bool
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Whether to print out progress information. Default: True.
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access.
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Attributes
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----------
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num_nodes: int
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Number of nodes
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num_rels: int
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Number of relation types
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Examples
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----------
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>>> dataset = FB15k237Dataset()
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>>> g = dataset.graph
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>>> e_type = g.edata['e_type']
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>>>
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>>> # get data split
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>>> train_mask = g.edata['train_mask']
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>>> val_mask = g.edata['val_mask']
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>>> test_mask = g.edata['test_mask']
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>>>
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>>> train_set = th.arange(g.num_edges())[train_mask]
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>>> val_set = th.arange(g.num_edges())[val_mask]
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>>>
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>>> # build train_g
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>>> train_edges = train_set
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>>> train_g = g.edge_subgraph(train_edges,
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relabel_nodes=False)
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>>> train_g.edata['e_type'] = e_type[train_edges];
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>>>
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>>> # build val_g
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>>> val_edges = th.cat([train_edges, val_edges])
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>>> val_g = g.edge_subgraph(val_edges,
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relabel_nodes=False)
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>>> val_g.edata['e_type'] = e_type[val_edges];
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>>>
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>>> # Train, Validation and Test
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"""
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def __init__(
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self,
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reverse=True,
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raw_dir=None,
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force_reload=False,
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verbose=True,
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transform=None,
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):
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name = "FB15k-237"
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super(FB15k237Dataset, self).__init__(
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name, reverse, raw_dir, force_reload, verbose, transform
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)
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def __getitem__(self, idx):
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r"""Gets the graph object
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Parameters
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-----------
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idx: int
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Item index, FB15k237Dataset has only one graph object
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Return
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-------
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:class:`dgl.DGLGraph`
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The graph contains
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- ``edata['e_type']``: edge relation type
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- ``edata['train_edge_mask']``: positive training edge mask
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- ``edata['val_edge_mask']``: positive validation edge mask
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- ``edata['test_edge_mask']``: positive testing edge mask
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- ``edata['train_mask']``: training edge set mask (include reversed training edges)
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- ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
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|
- ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
|
|
- ``ndata['ntype']``: node type. All 0 in this dataset
|
|
"""
|
|
return super(FB15k237Dataset, self).__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
r"""The number of graphs in the dataset."""
|
|
return super(FB15k237Dataset, self).__len__()
|
|
|
|
|
|
class FB15kDataset(KnowledgeGraphDataset):
|
|
r"""FB15k link prediction dataset.
|
|
|
|
The FB15K dataset was introduced in `Translating Embeddings for Modeling
|
|
Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
|
|
It is a subset of Freebase which contains about
|
|
14,951 entities with 1,345 different relations.
|
|
When creating the dataset, a reverse edge with
|
|
reversed relation types are created for each edge
|
|
by default.
|
|
|
|
FB15k dataset statistics:
|
|
|
|
- Nodes: 14,951
|
|
- Number of relation types: 1,345
|
|
- Number of reversed relation types: 1,345
|
|
- Label Split:
|
|
|
|
- Train: 483142
|
|
- Valid: 50000
|
|
- Test: 59071
|
|
|
|
Parameters
|
|
----------
|
|
reverse : bool
|
|
Whether to add reverse edge. Default True.
|
|
raw_dir : str
|
|
Raw file directory to download/contains the input data directory.
|
|
Default: ~/.dgl/
|
|
force_reload : bool
|
|
Whether to reload the dataset. Default: False
|
|
verbose : bool
|
|
Whether to print out progress information. Default: True.
|
|
transform : callable, optional
|
|
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
|
|
a transformed version. The :class:`~dgl.DGLGraph` object will be
|
|
transformed before every access.
|
|
|
|
Attributes
|
|
----------
|
|
num_nodes: int
|
|
Number of nodes
|
|
num_rels: int
|
|
Number of relation types
|
|
|
|
Examples
|
|
----------
|
|
>>> dataset = FB15kDataset()
|
|
>>> g = dataset.graph
|
|
>>> e_type = g.edata['e_type']
|
|
>>>
|
|
>>> # get data split
|
|
>>> train_mask = g.edata['train_mask']
|
|
>>> val_mask = g.edata['val_mask']
|
|
>>>
|
|
>>> train_set = th.arange(g.num_edges())[train_mask]
|
|
>>> val_set = th.arange(g.num_edges())[val_mask]
|
|
>>>
|
|
>>> # build train_g
|
|
>>> train_edges = train_set
|
|
>>> train_g = g.edge_subgraph(train_edges,
|
|
relabel_nodes=False)
|
|
>>> train_g.edata['e_type'] = e_type[train_edges];
|
|
>>>
|
|
>>> # build val_g
|
|
>>> val_edges = th.cat([train_edges, val_edges])
|
|
>>> val_g = g.edge_subgraph(val_edges,
|
|
relabel_nodes=False)
|
|
>>> val_g.edata['e_type'] = e_type[val_edges];
|
|
>>>
|
|
>>> # Train, Validation and Test
|
|
>>>
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
reverse=True,
|
|
raw_dir=None,
|
|
force_reload=False,
|
|
verbose=True,
|
|
transform=None,
|
|
):
|
|
name = "FB15k"
|
|
super(FB15kDataset, self).__init__(
|
|
name, reverse, raw_dir, force_reload, verbose, transform
|
|
)
|
|
|
|
def __getitem__(self, idx):
|
|
r"""Gets the graph object
|
|
|
|
Parameters
|
|
-----------
|
|
idx: int
|
|
Item index, FB15kDataset has only one graph object
|
|
|
|
Return
|
|
-------
|
|
:class:`dgl.DGLGraph`
|
|
|
|
The graph contains
|
|
|
|
- ``edata['e_type']``: edge relation type
|
|
- ``edata['train_edge_mask']``: positive training edge mask
|
|
- ``edata['val_edge_mask']``: positive validation edge mask
|
|
- ``edata['test_edge_mask']``: positive testing edge mask
|
|
- ``edata['train_mask']``: training edge set mask (include reversed training edges)
|
|
- ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
|
|
- ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
|
|
- ``ndata['ntype']``: node type. All 0 in this dataset
|
|
"""
|
|
return super(FB15kDataset, self).__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
r"""The number of graphs in the dataset."""
|
|
return super(FB15kDataset, self).__len__()
|
|
|
|
|
|
class WN18Dataset(KnowledgeGraphDataset):
|
|
r"""WN18 link prediction dataset.
|
|
|
|
The WN18 dataset was introduced in `Translating Embeddings for Modeling
|
|
Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
|
|
It included the full 18 relations scraped from
|
|
WordNet for roughly 41,000 synsets. When creating
|
|
the dataset, a reverse edge with reversed relation
|
|
types are created for each edge by default.
|
|
|
|
WN18 dataset statistics:
|
|
|
|
- Nodes: 40943
|
|
- Number of relation types: 18
|
|
- Number of reversed relation types: 18
|
|
- Label Split:
|
|
|
|
- Train: 141442
|
|
- Valid: 5000
|
|
- Test: 5000
|
|
|
|
Parameters
|
|
----------
|
|
reverse : bool
|
|
Whether to add reverse edge. Default True.
|
|
raw_dir : str
|
|
Raw file directory to download/contains the input data directory.
|
|
Default: ~/.dgl/
|
|
force_reload : bool
|
|
Whether to reload the dataset. Default: False
|
|
verbose : bool
|
|
Whether to print out progress information. Default: True.
|
|
transform : callable, optional
|
|
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
|
|
a transformed version. The :class:`~dgl.DGLGraph` object will be
|
|
transformed before every access.
|
|
|
|
Attributes
|
|
----------
|
|
num_nodes: int
|
|
Number of nodes
|
|
num_rels: int
|
|
Number of relation types
|
|
|
|
Examples
|
|
----------
|
|
>>> dataset = WN18Dataset()
|
|
>>> g = dataset.graph
|
|
>>> e_type = g.edata['e_type']
|
|
>>>
|
|
>>> # get data split
|
|
>>> train_mask = g.edata['train_mask']
|
|
>>> val_mask = g.edata['val_mask']
|
|
>>>
|
|
>>> train_set = th.arange(g.num_edges())[train_mask]
|
|
>>> val_set = th.arange(g.num_edges())[val_mask]
|
|
>>>
|
|
>>> # build train_g
|
|
>>> train_edges = train_set
|
|
>>> train_g = g.edge_subgraph(train_edges,
|
|
relabel_nodes=False)
|
|
>>> train_g.edata['e_type'] = e_type[train_edges];
|
|
>>>
|
|
>>> # build val_g
|
|
>>> val_edges = th.cat([train_edges, val_edges])
|
|
>>> val_g = g.edge_subgraph(val_edges,
|
|
relabel_nodes=False)
|
|
>>> val_g.edata['e_type'] = e_type[val_edges];
|
|
>>>
|
|
>>> # Train, Validation and Test
|
|
>>>
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
reverse=True,
|
|
raw_dir=None,
|
|
force_reload=False,
|
|
verbose=True,
|
|
transform=None,
|
|
):
|
|
name = "wn18"
|
|
super(WN18Dataset, self).__init__(
|
|
name, reverse, raw_dir, force_reload, verbose, transform
|
|
)
|
|
|
|
def __getitem__(self, idx):
|
|
r"""Gets the graph object
|
|
|
|
Parameters
|
|
-----------
|
|
idx: int
|
|
Item index, WN18Dataset has only one graph object
|
|
|
|
Return
|
|
-------
|
|
:class:`dgl.DGLGraph`
|
|
|
|
The graph contains
|
|
|
|
- ``edata['e_type']``: edge relation type
|
|
- ``edata['train_edge_mask']``: positive training edge mask
|
|
- ``edata['val_edge_mask']``: positive validation edge mask
|
|
- ``edata['test_edge_mask']``: positive testing edge mask
|
|
- ``edata['train_mask']``: training edge set mask (include reversed training edges)
|
|
- ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
|
|
- ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
|
|
- ``ndata['ntype']``: node type. All 0 in this dataset
|
|
"""
|
|
return super(WN18Dataset, self).__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
r"""The number of graphs in the dataset."""
|
|
return super(WN18Dataset, self).__len__()
|
|
|
|
|
|
def load_data(dataset):
|
|
r"""Load knowledge graph dataset for RGCN link prediction tasks
|
|
|
|
It supports three datasets: wn18, FB15k and FB15k-237
|
|
|
|
Parameters
|
|
----------
|
|
dataset: str
|
|
The name of the dataset to load.
|
|
|
|
Return
|
|
------
|
|
The dataset object.
|
|
"""
|
|
if dataset == "wn18":
|
|
return WN18Dataset()
|
|
elif dataset == "FB15k":
|
|
return FB15kDataset()
|
|
elif dataset == "FB15k-237":
|
|
return FB15k237Dataset()
|